Abstract
Abstract
Background
Health disparities persist, posing significant health, social and economic challenges. Digital health technologies (DHTs) present a promising opportunity to address these inequities and advance health equity. Despite this potential, a comprehensive and structured overview of existing frameworks and guidelines on advancing health equity and a clear understanding of the potential of DHTs in their implementation to systematically close the healthcare gap is yet to be done.
Objective
To this end, our objectives are twofold: first, to identify frameworks and guidelines that promote health equity and second, to pinpoint the role of DHTs as an avenue for their implementation. We conducted a scoping review informed by Arksey and O'Malley’s five-stage framework, methodological guidelines by the Joanna Briggs Institute and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.
Sources of evidence
A comprehensive search was conducted across seven databases on 6 December 2023: PubMed, EMBASE, Cochrane, PsycINFO, Scopus, Web of Science and WISO.
Eligibility criteria
We included primary and secondary studies published in English between 2010 and 2023 focusing on advancing health equity for priority populations.
Charting methods
For the analysis, we applied multistaged coding approaches to answer our twofold objective.
Results
The search identified 6419 studies, of which 38 met our final inclusion criteria and were included in this review. We extracted 559 recommendations on advancing health equity and synthesised these into 82 distinct recommendations across five levels of initiative and 19 areas of initiative. Thereby, 24% of the included studies explicitly mentioned the use of (digital) technology with 10 impact opportunities on advancing health equity.
Conclusion
Our synthesis offers key insights into the advancement of health equity across different levels of initiative and the role of DHTs in their implementation. This offers practitioners and researchers alike a comprehensive overview to make health equity advancement more tangible and actionable.
Registration details
Keywords: Health Equity, Health informatics, Digital Technology, PUBLIC HEALTH
STRENGTHS AND LIMITATIONS OF THIS STUDY.
The application of a rigorous, well-established methodological framework ensures the production of a high-quality scoping review.
A comprehensive search in multiple databases allows an extensive mapping of the current landscape of health equity frameworks and guidelines and the potential of digital health technologies in implementing them.
Selective inclusion of the grey literature further strengthens our review by reducing publication bias and enhancing the comprehensiveness of the findings.
The synthesis is limited to articles published in English, which increases the risk of missing relevant insights from local initiatives across the globe.
Introduction
Despite notable progress in research, practice and policy, health disparities continue to persist. Priority populations, including those with low socioeconomic status, individuals from diverse racial and ethnic backgrounds, sexual and gender minorities and people living with disabilities, experience notably higher impacts from non-communicable diseases compared with non-marginalised counterparts.1,7 Women spend 25% more time in poor health than men.8 In addition to the higher burden, priority populations frequently encounter barriers to accessing healthcare or face substandard healthcare services,9,12 exacerbating disparities in health outcomes and perpetuating entrenched social inequities.13,15 This is not only a societal and health challenge but also comes at an economic loss. In the USA alone, the yearly excess healthcare costs due to racial health disparities are estimated at US$93 billion16 and closing the global women’s health gap promises a US$1 trillion opportunity.8
Hence, it is imperative to identify and implement systematic approaches to advance health equity globally. Per the WHO, health equity refers to ‘the absence of unfair, unavoidable or remediable differences among groups of people, whether those groups are defined socially, economically, demographically or geographically or by other dimensions of inequality. Health equity is achieved when everyone can attain their full potential for health and well-being’.17 Thereby, factors influencing health equity are not only healthcare delivery itself but are multifaceted and multidimensional, with a plethora of frameworks conceptualising the underlying complexity. For example, research indicates that social determinants of health, that is, conditions in which people are born, grow, work, live and age, contribute to approximately 30–55% of health outcomes.18 This underscores the critical necessity to take a comprehensive approach to battling persistent health disparities.
Digital health technologies (DHTs) offer a promising avenue for enhancing health equity. DHT thereby refers to a ‘system that uses computing platforms, connectivity, software and sensors for healthcare and related uses’.19 By leveraging technology, DHTs hold the potential to address numerous traditional barriers to healthcare access, including geographical constraints, transportation limitations, appointment availability issues and the financial burden associated with healthcare expenses.20,22 Moreover, if affordable, DHTs can help reduce bias and inequality within the healthcare system, as they are not susceptible to the implicit social and cultural biases that can influence traditional healthcare delivery. Furthermore, the inherent technological flexibility in modifying and customising DHTs underscores their immense potential to adapt to diverse cultures, languages and contextual needs.23 This has been demonstrated to not only enhance treatment effects but also foster sustained engagement across numerous studies.24 25 With recent advancements in generative artificial intelligence (AI) (e.g., large language models),26,28 such adaptations and even personalisation of treatment are expected to become more feasible. Consequently, DHTs have the potential to assume an active role in advancing health equity across multiple dimensions. Simultaneously, the integration of AI comes with its own set of challenges regarding inherent biases. AI models are trained based on historical data that may perpetuate existing health disparities29 30 or biases against certain groups.31,33 In addition to data biases, there can also be algorithmic biases that arise during the design and development of the underlying AI algorithms, resulting in unwanted outcomes for priority populations.34,36 As such, increasing reliance on digital healthcare delivery may risk widening the digital divide, for example, disadvantaging populations with limited access to technology,37 lower digital (health) literacy,38 lower socioeconomic status39 40 or, as outlined above, underrepresented data in AI models.29 Thus, it becomes increasingly important to centre equity considerations in the heart of DHT development.29 30
Despite the potential of DHTs to promote health equity, only a small body of knowledge exists on how to systematically leverage DHTs to close the healthcare gap. Previous studies have explored how individual DHTs can be developed, deployed and integrated into existing healthcare systems to ensure equitable access and outcomes for all populations, regardless of socioeconomic status, race, ethnicity or other demographic characteristics.1341,43 However, most of the work focuses on individual solutions or adaptations of existing solutions to specific geographical and cultural contexts.44,46 While cultural adaptation of treatments undoubtedly plays a vital role in advancing health equity, cultural adoption still requires substantial efforts, and considerable variations in adaptation approaches and their reporting methods47 hinder adaptations at scale. To systematically improve health outcomes for priority populations, it becomes imperative to prioritise health equity as a fundamental objective and to reframe approaches to design, evaluate, implement and scale DHTs.2248,52 This is essential to avoid inadvertently exacerbating disparities in health outcomes, as outlined by the inverse care law, which describes the phenomenon that individuals with greater resources often possess better access and awareness of these interventions compared with those with fewer resources.53
Recent research has paved the way for advancing health disparity studies into the digital era, for example, by broadening the health disparity framework to encompass a digital domain50 or by establishing digital determinants of health within the context of social determinants of health frameworks.49 These efforts make substantial contributions to unravelling the mechanisms and scale of health disparities in contemporary times. Nevertheless, there remains a notable absence of a structured overview and analysis of current frameworks and guidelines for advancing health equity, along with a systematic mapping of DHTs.
The current work aims to close this gap by conducting a scoping review and answering the following research questions:
Which frameworks and guidelines exist to advance health equity and what recommendations and initiatives do they propose?
What is the role attributed to DHTs in implementing these frameworks and guidelines?
By identifying, analysing and synthesising overarching and population-specific frameworks and guidelines to advance health equity, this research holds the potential to inform policies and practices alike. Such insights can guide the development and delivery of DHTs aimed at promoting health equity, ensuring they are accessible, affordable and effective for all individuals.
Methods
To answer the identified research questions, we conducted a scoping review informed by the Arksey and O'Malley54 five-stage process, established methodological guidelines provided by the Joanna Briggs Institute (JBI)55,57 and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews. The protocol for this review58 has been published in 2024 and the study was preregistered on the Open Science Framework Registry on 20 November 2023 (https://osf.io/94pht).
Information sources and search strategy
Search terms and alternative keywords were derived from a preliminary search. The search strategy was finalised with the help of two librarians. For health equity, we made the conscious decision to focus on the positive framing of health equity, not including health disparities and their respective synonyms, as we wanted to focus on research that outlines the solution space to advancing health equity rather than frameworks and guidelines that contextualise and summarise the problem space of health disparities (see table 1 for search terms and online supplemental material SM1 for full search terms for one database). A comprehensive search was conducted across seven databases on 6 December 2023: PubMed, EMBASE, Cochrane, PsycINFO, Scopus, Web of Science and WISO.
Table 1. Search strings based on alternative keywords.
| ID | Description | Search terms |
|---|---|---|
| 1 | Frameworks and guidelines | framework* OR guide* OR principle* OR approach* OR polic* OR standard* OR strateg* OR directive* OR methodolog* OR protocol* OR practice* OR recommend* OR consider* OR imperative* OR agenda OR synthesis |
| 2 | Health equity | “health equit*” OR “health equalit” OR “health justice” OR “health parit*” |
Eligibility criteria
The identified articles were screened following specific eligibility criteria, which follow the JBI guidelines outlining population, concept, context and evidence sources and are summarised in table 2. The exclusion criteria are as follows: advancement of health equity not as primary study outcome; full-text not accessible; full-text not available in English; other study types: editorials, opinion papers, grey literature, dissertations, conference papers, comments and letters, published without peer review.
Table 2. Eligibility criteria for inclusion.
| Eligibility criteria | Description |
|---|---|
| Population | In this review, we consider studies focusing on priority populations typically affected by health disparities. Examples include but are not limited to marginalisation due to race or ethnicity, religion, age, gender, sexual orientation or gender identity, cognitive, sensory or physical disability and/or financial and socioeconomic status.65 |
| Concept | We consider studies that focus on advancing health equity for priority populations. We will focus on frameworks and guidelines that aim to advance equity as a primary study outcome. Acknowledging that health equity may not be used explicitly, other terms aligning with health equity will be included, such as health equality or health justice (for a complete list of terms, see the search strategy in table 1). The same applies to the terms ‘frameworks’ and ‘guidelines’. Other terms may include principles, approaches, policies, standards, strategies, directives or methodologies to advance health equity. This review will exclude sources not explicitly positioned as frameworks and guidelines to advance health equity as a primary outcome but only touch on health equity peripherally. |
| Context | The search will not be limited to specific geographical, cultural or social settings to ensure a diverse perspective. However, only studies published in English will be considered. |
| Evidence sources | This review will consider qualitative, quantitative and mixed-method studies. Additionally, all types of reviews (e.g., systematic, scoping, umbrella, narrative) and selective grey literature (e.g., government documents, policy documents) from selective institutions (e.g., WHO) will be considered. Studies from 2010 and onwards will be included, as this marks a shift in both language and research emphasis from health disparities (focus on problem identification) to health equity (focus on solutions).66 In addition, this coincides with the emergence of early digital health solutions and the increasing availability of smartphones and mobile apps, ringing in the second wave of DHTs.67 Inclusion of the relevant literature will not be restricted by regional origin. |
Screening, data extraction and analysis
Following comprehensive database searches, all citations were exported to Citavi V.6.17. We then undertook a two-stage screening process. First, coauthors LB and EP independently assessed the titles and abstracts of the retrieved studies with Rayyan (https://www.rayyan.ai/) based on the predefined eligibility criteria. During the second stage, full-text evaluation was conducted by paired authors to mitigate selection bias. Additionally, any review data sources that overlapped with primary studies were evaluated to determine the uniqueness of the evidence. Any differences were resolved through discussion or consultation with a third coauthor. The selection was meticulously recorded, including a PRISMA flow diagram to detail exclusions.
A customised Excel workbook was used for data extraction, initially testing it on a few studies to iteratively refine it. Beyond generic information commonly used in scoping reviews, such as the author’s name, publication year, study type, publication title and results,56 we extracted and analysed the applied health equity definition, the application domain (e.g., cardiology, COVID-19), the level of outcome representation, initiative level of proposed recommendations and the health equity advancement recommendations themselves.
For the qualitative analysis of the extracted recommendations, we applied a multistaged coding approach, which is an established methodology for analysing qualitative data. First, LB allocated first-order codes to all extracted data to then systematically identify overarching themes to derive the synthesised recommendations presented in this study. For the first level of structure, we leveraged the level of initiative extracted from the original sources, which can be summarised into policy, organisations and systems, communities, individual and intervention. LB then clustered the synthesised recommendations into areas of initiative within these initial levels, resulting in a three-level coding tree. Based on the initial analysis, a coding book was developed to validate the coding approach and results through a coauthor (for details see online supplemental material SM2). In an intensive coding workshop, LB and MN discussed the coding approach and results, refining and finalising the outcome dimensions.
To analyse the role of DHTs in advancing health equity, all recommendations outlining the use of digital (health) technologies as a way to advance health equity were marked for further analysis during the initial first-order coding. This subset of recommendations then underwent an analogous iterative coding approach of first-order code allocation and second-order code synthesis into impact opportunities. Finally, the identified impact opportunities were clustered into three impact dimensions: individual, healthcare provider and intersection of individuals and healthcare providers.
Patient and public involvement
Neither patients nor the public were involved in the design, reporting or dissemination of this research project.
Results
Study selection
The search across the seven databases identified 6419 records. After the removal of duplicates and initial title and abstract screening, the assessment of 112 full-text papers for their eligibility followed, out of which 82 were excluded due to misaligned focus: 52 studies did not have the advancement of health equity as the primary study focus (e.g., only as a sublevel dimension), 22 studies focused on outcomes that did not align with our inclusion criteria (e.g., not providing recommendations for advancing health equity), five studies had the wrong publication type (e.g., dissertations) and three studies were review articles which were excluded in the final selection to avoid double-counting of primary research. However, these three review articles, as well as the final selection of 30 records, were used to identify additional relevant records missed by the initial search strategy via forward and backward search, which identified an additional nine unique records. Of these, one record was a review article which was again excluded after a backward search yielded no additional insights. Finally, our comprehensive review yielded 38 relevant studies. The study selection process is visualised in a PRISMA flow chart(figure 1). The data supporting the results detailed below will be made available on reasonable request from the corresponding author.
Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow chart.
Characteristics of included studies
The general characteristics of the included studies are reported in table 3. More than two-thirds (n=28, 74%) of the studies were published from 2020 onwards, highlighting the increasing emphasis on health equity in recent years. In total, 559 individual initiatives or recommendations were extracted across all studies with an average of 15 recommendations per study. Thereby, the studies predominantly focused on initiatives on the organisation and system levels (n=15, 39%), followed by multilevel frameworks and recommendations (n=11, 29%), interventions (n=7, 18%) and policy and government (n=5, 13%). None of the included studies focused solely on the community or individual level. Most of the papers used visual representation to convey their outcome, with 42% using simple visuals (e.g., tables) and 37% using complex visuals (e.g., frameworks). The populations covered across the studies were people of color (n=5, 13%), rural population (n=2, 5%), low socioeconomic status (n=1) and women (n=1), but mostly unspecified (n=29, 76%). The domains of health equity action span across cancer (n=2), cardiology (n=1), COVID-19 (n=2), mental health (n=2), digital health (n=3) and others (n=2) or unspecified (n=26).
Table 3. General characteristics of included studies (n=38).
| General characteristics of included studies (n=38) | ||
|---|---|---|
| Characteristics | Number (n=38) | Percentage (%) |
| Publication year | ||
| 2010–2014 | 2 | 5% |
| 2015–2019 | 8 | 21% |
| 2020+ | 28 | 74% |
| Publication type | ||
| Journal article | 35 | 92% |
| Government or research station report | 3 | 8% |
| Level of initiative | ||
| Multilevel | 11 | 29% |
| Policy | 5 | 13% |
| Organisations and systems | 15 | 39% |
| Intervention | 7 | 18% |
| Outcome representation | ||
| Textual representation | 8 | 21% |
| Simple visual representation (tables etc) | 16 | 42% |
| Complex visual representation (frameworks etc) | 14 | 37% |
| Target population | ||
| Unspecified | 29 | 76% |
| People of color | 5 | 13% |
| Rural population | 2 | 5% |
| Low socioeconomic status | 1 | 3% |
| Women | 1 | 3% |
| Domain | ||
| Cancer | 2 | 5% |
| Cardiology | 1 | 3% |
| COVID-19 | 2 | 5% |
| Digital Health | 3 | 8% |
| Mental health | 2 | 5% |
| Other | 2 | 5% |
| Unspecified | 26 | 68% |
| Health equity definition | ||
| Yes | 20 | 53% |
| No | 18 | 47% |
| Number of extracted health equity recommendations | ||
| Total number | 559 | |
| Minimum per paper | 3 | |
| Maximum per paper | 61 | |
| Average per paper | 15 | |
A full overview of all included studies and their key properties can be found in the online supplemental material (SM3).
Health equity advancement recommendations
In addressing our first research question, we identified 82 distinct recommendations across five levels and 19 areas of initiatives to actively advance health equity (see online supplemental material SM4). The levels of initiatives are policy and government, organisations and systems, community, individual and intervention, which is in line with previous works (e.g., studies by Lyles et al and Shaw et al49 59). A description of each level of initiative applied in our work can be found in the online supplemental material SM2 and a visual representation of the level and areas of initiative can be found in figure 2.
Figure 2. Identified levels and areas of initiative to enhance health equity.
Policy and government
At the level of policy and government, recommendations can be clustered into the sublevels access to and delivery of care (n=7), infrastructure and basic services (n=5), social and economic support (n=4), social determinants of health (SDoH) (n=4), community engagement and partnership (n=4) and multisectoral collaboration and governance (n=3). Access to and delivery of care recommendations are centred on improving healthcare accessibility, quality and delivery methods to ensure everyone receives appropriate care. Infrastructure and basic service recommendations focus on providing access to essential services such as broadband internet, housing, public transport, etc, and establishing healthy environments in neighbourhoods and workplaces. Recommendations in the social and economic support cluster focus on addressing income and wealth inequalities, for example, through additional support for the disadvantaged, social redistribution policies or social protection schemes. SDoH recommendations recognise the broader societal factors influencing health outcomes and advocate for their inclusion in healthcare standards. Community engagement and partnership recommendations encourage building trust through partnerships, clear communication strategies and proactive engagement with diverse communities to ensure their needs are addressed and they play a role in decision-making and policy processes. Finally, recommendations on multisectoral collaboration and governance focus on forming coalitions with non-traditional allies, strategic policy advocacy, increasing cross-sectoral collaboration and leadership commitment to eliminating disparities.
Organisations and systems
Recommendations at the organisation and system levels can be clustered into the sublevels data collection and analysis (n=4), quality improvement and system integration (n=5), workforce diversity and inclusion (n=5), capacity and capability building (n=3), community engagement and partnership (n=4) and policy advocacy and structural change (n=10). Data collection and analysis recommendations focus on the systematic gathering, processing and interpretation of disaggregated health-related data to inform decision-making and interventions aimed at improving health equity. Recommendations on quality improvement and system integration emphasise evaluating initiatives against predefined equity metrics, building quality improvement infrastructure, and embedding interventions into the organisation’s structure. Workforce diversity and inclusion recommendations centre on recruiting and retaining a diverse workforce and promoting opportunities for career advancement and leadership among under-represented groups. Capacity and capability-building recommendations focus on training programmes, technical assistance and organisational development initiatives to build the capacity and capabilities of healthcare providers, (health) organisations and other stakeholders. Recommendations on community engagement and partnership emphasise the importance of engaging with community stakeholders and establishing partnerships with various organisations inside and outside the healthcare system. Finally, policy advocacy and structural change recommendations were the most varied. Centred on local, state and national change advocacy, they emphasise promoting policies that enable health for all, reviewing and addressing institutional racism and challenging assumptions and power structures within organisations.
Community
At the community level, recommendations focus on support systems and partnerships (n=2) and accessibility and inclusivity (n=2). Support systems and partnerships recommendations focus on supporting caregivers, building reciprocal relationships and investing in community-based organisations and partnerships to promote health equity through comprehensive support systems. Recommendations on accessibility and inclusivity emphasise ensuring equitable access to healthcare resources and services by increasing linguistic and cultural support and implementing enrolment processes that remove barriers, ultimately striving for inclusivity in healthcare delivery and decision-making.
Individual
Recommendations at the individual level (n=3) focus on empowering individuals to take charge of their own health through health literacy and digital literacy training, providing them autonomy over their own health data and decisions and addressing individuals’ attitudes and behaviours that affect their health, for example, through stigma.
Intervention
At the intervention level, recommendations can be clustered based on a lifecycle perspective, starting with needs assessment and formative research (n=3), followed by intervention design and development (n=5), implementation planning and adaptation (n=6) and finally, evaluation and dissemination (n=3). Needs assessment and formative research recommendations encompass activities related to understanding the needs, preferences and contextual factors of the target population through research and community engagement. Intervention design and development recommendations include tasks such as selecting theoretical frameworks, identifying culturally relevant intervention components and designing delivery formats. Recommendations on implementation planning and adaptation centre on activities such as identifying implementation strategies, assessing readiness for implementation, adapting interventions based on contextual factors and developing implementation plans with the community. Finally, recommendations of evaluation and dissemination focus on evaluating interventions, monitoring their implementation and outcomes, disseminating intervention findings, scaling up effective interventions and ensuring their sustainability over time.
Role of DHTs in the advancement of health equity
To answer our second research question, we inspected the subset of studies identified to mention digital technologies. Our analysis revealed nine studies (24%) that explicitly mentioned the use of DHTs to advance health equity, and the iterative coding approach identified 10 impact opportunities across the three impact dimensions: individual, intersection between individuals and healthcare provider and healthcare provider. At the individual level, impact opportunities include improving access to care, increasing the availability of care, increasing service offerings, improving health literacy and providing information, providing care closer to home and engaging patients outside of clinics. Improving access to care focuses predominantly on leveraging technology for remote and virtual care through telemedicine. Increasing availability of care refers to on-demand availability outside of usual business hours through digital technologies. Increasing service offerings encompasses the complementary use of digital technologies in addition to inperson care for a more comprehensive care approach, as well as developing adaptive, personalised treatment options. Improving health literacy refers to leveraging digital technologies to provide information and skill-based training to individuals. Providing care closer to home focuses on leveraging digital technologies for innovative care approaches and remote care such as remote patient monitoring. Finally, engaging patients outside of the clinics refers to leveraging communication technologies, text messaging etc, to interact with patients in their own living environment and practices. At the intersection between individuals and healthcare providers, impact opportunities include improving data collection and providing data access and improving patient-provider interactions. Improving data collection and providing data access refers to improving digital technologies such as the electronic health records for data sharing between healthcare professionals and individuals as well as improving the autonomy of individuals over their own data. Improving patient-provider interactions focuses on leveraging technology to have high-quality, seamless remote interactions (e.g., integrated translation tools). Finally, at the healthcare provider level, impact opportunities include facilitating information exchange across the healthcare ecosystem and improving team-based care. Facilitating information exchange across the healthcare ecosystem focuses on leveraging technology to digitise health records across all service providers and to connect DHTs with other healthcare programmes. Improving team-based care refers to the use of technology to make the collaboration between physicians and patients within a team-based care approach more effective and efficient. A full map of impact opportunities and supporting sources can be found in the online supplemental material SM5. Thereby, improving access to care was mentioned most (n=5), closely followed by facilitated information exchange (n=4) and increasing availability (n=3), increasing service offerings (n=3) and improving health literacy (n=3). Of the top five mentioned impact opportunities, 80% (n=4) target the individual. Furthermore, it is noteworthy that only potential opportunities and advantages of DHTs emerged. Potential challenges or risks of DHTs to health equity advancement were not explicitly discussed in the included papers.
Discussion
This is the first scoping review to provide a structured overview and analysis of existing recommendations on advancing health equity and to assess the role of DHTs. We identified 38 relevant articles, with nearly two-thirds published in the past 4 years, suggesting increasing recognition of the topic. Our findings underscore the need for systematic change at organisational, system and policy levels, shifting responsibility away from individuals and communities. Notably, none of the included studies focus solely on individual-level interventions, and only a few incorporate them within multilevel frameworks. Overall, this aligns with previous work that has criticised interventions that solely focus on the individual as they often fall short of acknowledging the upstream and downstream effects of health disparities (e.g., studies by Richardson et al, Brown et al, Agurs-Collins et al and Alvidrez et al).5060,62
Key themes and their implications
While there was generally a wide range of recommendations covering various topics and initiatives, we identified five widely supported recommendations.
One key theme is healthcare providers’ capabilities and resulting processes. Recommendations R2.2.1 and R2.4.1 highlight the need for and importance of culturally sensitive care delivery and indicate the risk of provider bias for health equity. Practitioners can leverage these recommendations to refine healthcare provider education, training programmes and service delivery models.
Another critical area is community engagement. Recommendation R2.5.4 suggests involving community stakeholders across sectors and disciplines at every stage. The concept of community engagement is generally highly present across nearly all levels of initiatives (cf. recommendations R1.5.3, R3.2.2, R5.1.3, R5.2.2, R5.3.6). The aim is to bring research and decision-making closer to the affected individuals and groups, ensuring tailored interventions, user-centric design and overall sustainability of the efforts.
Disaggregated data collection is also a key priority, as emphasised in recommendation R2.1.3. This allows for identifying disparities among different racial and ethnic groups and across various SDoH, which might otherwise go unnoticed, which is in line with recent calls for action.63 Further, it could help to gain insights into the underlying factors, which is essential for developing targeted interventions that address the root causes and allow for evaluating the effectiveness of interventions over time, promoting accountability.
Finally, user-centred intervention design is another essential consideration. Recommendation R5.2.1 suggests tailoring invention content and behaviour change mechanisms based on user characteristics such as culture, learning styles and overall literacy. Tailoring interventions to the target population has been shown to increase effectiveness and sustain user engagement over time.24 25 Practitioners can leverage these insights to design and develop more sustainable interventions.
The role of DHTs
Although 24% of the included studies actively mention the use of technology to advance health equity, they largely focus on benefits such as access to care, availability of care, increased service offerings and health literacy training. While these studies all focus on the opportunities that arise from leveraging technologies for health equity advancement, they predominantly fail to discuss the potential of exacerbating the digital divide or widening the gap in health outcomes as per the inverse care law and how to address this. For practitioners, this highlights the need for equity-first design approaches in DHT development to avoid unintended exclusion.
Limitations and future research
This scoping review also has some limitations which open opportunities for future research. While we developed a comprehensive search strategy, our synthesis is limited to articles published in English, which increases the risk of missing relevant insights from local initiatives across the globe. Furthermore, we limited our search keywords to the positive framing of equity to focus on the solution space of advancing health equity, disregarding studies that contextualise and summarise the problem space of health disparities. We tried to minimise the extent of omission of the relevant literature through extensive forward and backward searches of included studies and other review articles.
While we synthesised available scientific knowledge, the lack of direct engagement with priority populations means firsthand insights may be underrepresented. Participatory research methods should be prioritised in future studies to ensure that interventions are designed with, rather than for, priority communities.
Another key gap is the limited discussion on potential risks associated with DHTs. While many studies highlight the benefits of technology in expanding healthcare access, few explicitly examine how digital health interventions may inadvertently widen disparities. Future research should assess whether existing recommendations mitigate or exacerbate the digital divide, particularly among populations with limited digital literacy or access. This also includes investigations on how to reach and engage priority populations via novel approaches, such as the growing number of influencers in social media,64 as one of many attempts to reduce the digital divide. Given the rising interest in DHTs, the increasing number of available market solutions and the rising interest in health equity in recent years, future work could further explore both upstream and downstream implications of technology in advancing health equity in more depth.
Furthermore, as of today, there is a lack of actionable guidelines and standards for both research and practice on how to develop DHTs from a health equity perspective. However, to systematically improve health outcomes for priority populations, prioritising health equity as a guiding fundamental objective becomes imperative to develop or reframe approaches to designing, implementing, evaluating and disseminating both digital and non-digital health interventions.48,52 As such, future research could leverage the recommendations identified in this study to derive actionable design guidance, evaluate the impact of implementing these recommendations during design and development of DHTs or provide prioritisation tools to guide implementation.
Conclusion
This review is the first to synthesise recommendations on health equity advancement across research disciplines. As such, we created a comprehensive overview to make health equity advancement more tangible and actionable and a valuable resource to identify relevant recommendation frameworks, for example, for specific domains or populations. We also provide a directory of which of these frameworks consider and discuss the role of DHTs in advancing health equity. Ultimately, by identifying consensus-driven strategies and highlighting gaps, our work provides a foundation for developing more actionable, equity-centred interventions and policies.
Supplementary material
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
prepub: Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2025-099306).
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Not applicable.
Ethics approval: Not applicable.
Patient and public involvement: Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.
Data availability statement
Data are available upon reasonable request.
References
- 1.Blondeel K, Say L, Chou D, et al. Evidence and knowledge gaps on the disease burden in sexual and gender minorities: a review of systematic reviews. Int J Equity Health. 2016;15:16. doi: 10.1186/s12939-016-0304-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Carrilero N, García‐Altés A, Mendicuti VM, et al. Do governments care about socioeconomic inequalities in health? Narrative review of reports of EU‐15 countries. European Policy Analysis. 2021;7:521–36. doi: 10.1002/epa2.1124. [DOI] [Google Scholar]
- 3.Krahn GL, Walker DK, Correa-De-Araujo R. Persons with disabilities as an unrecognized health disparity population. Am J Public Health. 2015;105 Suppl 2:S198–206. doi: 10.2105/AJPH.2014.302182. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Mackenbach JP, Stirbu I, Roskam A-JR, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med. 2008;358:2468–81. doi: 10.1056/NEJMsa0707519. [DOI] [PubMed] [Google Scholar]
- 5.Sharrocks K, Spicer J, Camidge DR, et al. The impact of socioeconomic status on access to cancer clinical trials. Br J Cancer. 2014;111:1684–7. doi: 10.1038/bjc.2014.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Vilsaint CL, NeMoyer A, Fillbrunn M, et al. Racial/ethnic differences in 12-month prevalence and persistence of mood, anxiety, and substance use disorders: Variation by nativity and socioeconomic status. Compr Psychiatry. 2019;89:52–60. doi: 10.1016/j.comppsych.2018.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Santiago CD, Kaltman S, Miranda J. Poverty and mental health: how do low-income adults and children fare in psychotherapy? J Clin Psychol. 2013;69:115–26. doi: 10.1002/jclp.21951. [DOI] [PubMed] [Google Scholar]
- 8.World Economic Forum, McKinsey Health Institute Closing the Women’s Health Gap: A $1 Trillion Opportunity to Improve Lives and Economies. https://www3.weforum.org/docs/WEF_Closing_the_Women%E2%80%99s_Health_Gap_2024.pdf Available.
- 9.Cook BL, Trinh N-H, Li Z, et al. Trends in Racial-Ethnic Disparities in Access to Mental Health Care, 2004–2012. PS. 2017;68:9–16. doi: 10.1176/appi.ps.201500453. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Dahlhamer JM, Galinsky AM, Joestl SS, et al. Barriers to Health Care Among Adults Identifying as Sexual Minorities: A US National Study. Am J Public Health. 2016;106:1116–22. doi: 10.2105/AJPH.2016.303049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Evans-Lacko S, Aguilar-Gaxiola S, Al-Hamzawi A, et al. Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys. Psychol Med. 2018;48:1560–71. doi: 10.1017/S0033291717003336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Scheer J, Kroll T, Neri MT, et al. Access Barriers for Persons with Disabilities. J Disabil Policy Stud. 2003;13:221–30. doi: 10.1177/104420730301300404. [DOI] [Google Scholar]
- 13.Brewer LC, Fortuna KL, Jones C, et al. Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health. JMIR Mhealth Uhealth. 2020;8:e14512. doi: 10.2196/14512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Baciu A, Negussie Y, Geller A, et al., editors. Communities in Action: Pathways to Health Equity. 2017. [PubMed] [Google Scholar]
- 15.Smedley BD, Stith AY, Nelson AR. Unequal treatment: confronting racial and ethnic disparities in health care. 2003 [PubMed]
- 16.Kellogg Foundation Business Case for Racial Equity. 2018. https://altarum.org/sites/default/files/WKKellogg_Business-Case-Racial-Equity_National-Report_2018.pdf Available.
- 17.World Health Organization Health equity. 2023. [18-Aug-2023]. https://www.who.int/health-topics/health-equity Available. Accessed.
- 18.World Health Organization Social determinants of health. 2023. https://www.who.int/health-topics/social-determinants-of-health Available.
- 19.International Organization for Standardization ISO/TR 11147:2023(en), Health informatics — Personalized digital health — Digital therapeutics health software systems. 2023. https://www.iso.org/obp/ui/en/#iso:std:83767:en Available.
- 20.Jacobson NC, Quist RE, Lee CM, et al. Digital Therapeutics for Mental Health and Addiction. Elsevier; 2023. Using digital therapeutics to target gaps and failures in traditional mental health and addic-tion treatments; pp. 5–18. [Google Scholar]
- 21.Cummings JR, Allen L, Clennon J, et al. Geographic Access to Specialty Mental Health Care Across High- and Low-Income US Communities. JAMA Psychiatry. 2017;74:476. doi: 10.1001/jamapsychiatry.2017.0303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Schlieter H, Gand K, Marsch LA, et al. Editorial: Scaling-up health-IT-sustainable digital health implementation and diffusion. Front Digit Health. 2024;6:1296495. doi: 10.3389/fdgth.2024.1296495. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Yardley L, Morrison L, Bradbury K, et al. The person-based approach to intervention development: application to digital health-related behavior change interventions. J Med Internet Res. 2015;17:e30. doi: 10.2196/jmir.4055. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Hall GCN, Ibaraki AY, Huang ER, et al. A Meta-Analysis of Cultural Adaptations of Psychological Interventions. Behav Ther. 2016;47:993–1014. doi: 10.1016/j.beth.2016.09.005. [DOI] [PubMed] [Google Scholar]
- 25.Harper Shehadeh M, Heim E, Chowdhary N, et al. Cultural Adaptation of Minimally Guided Interventions for Common Mental Disorders: A Systematic Review and Meta-Analysis. JMIR Ment Health. 2016;3:e44. doi: 10.2196/mental.5776. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Thirunavukarasu AJ, Ting DSJ, Elangovan K, et al. Large language models in medicine. Nat Med. 2023;29:1930–40. doi: 10.1038/s41591-023-02448-8. [DOI] [PubMed] [Google Scholar]
- 27.The Lancet AI in medicine: creating a safe and equitable future. The Lancet. 2023;402:503. doi: 10.1016/S0140-6736(23)01668-9. [DOI] [PubMed] [Google Scholar]
- 28.Lee P, Bubeck S, Petro J. Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine. N Engl J Med. 2023;388:1233–9. doi: 10.1056/NEJMsr2214184. [DOI] [PubMed] [Google Scholar]
- 29.Goldberg CB, Adams L, Blumenthal D, et al. To do no harm - and the most good - with AI in health care. Nat Med. 2024;30:623–7. doi: 10.1038/s41591-024-02853-7. [DOI] [PubMed] [Google Scholar]
- 30.Hastings J. Preventing harm from non-conscious bias in medical generative AI. Lancet Digit Health. 2024;6:e2–3. doi: 10.1016/S2589-7500(23)00246-7. [DOI] [PubMed] [Google Scholar]
- 31.Rajkomar A, Hardt M, Howell MD, et al. Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med. 2018;169:866–72. doi: 10.7326/M18-1990. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Norori N, Hu Q, Aellen FM, et al. Addressing bias in big data and AI for health care: A call for open science. Patterns (N Y) 2021;2:100347. doi: 10.1016/j.patter.2021.100347. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Timmons AC, Duong JB, Simo Fiallo N, et al. A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health. Perspect Psychol Sci. 2023;18:1062–96. doi: 10.1177/17456916221134490. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.DeCamp M, Lindvall C. Mitigating bias in AI at the point of care. Science. 2023;381:150–2. doi: 10.1126/science.adh2713. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Flores L, Kim S, Young SD. Addressing bias in artificial intelligence for public health surveillance. J Med Ethics. 2024;50:190–4. doi: 10.1136/jme-2022-108875. [DOI] [PubMed] [Google Scholar]
- 36.Straw I, Callison-Burch C. Artificial Intelligence in mental health and the biases of language based models. PLoS ONE. 2020;15:e0240376. doi: 10.1371/journal.pone.0240376. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Dijk JAGM . Digital Divide: Impact of Access. Wiley; 2017. [Google Scholar]
- 38.Mackert M, Mabry-Flynn A, Champlin S, et al. Health Literacy and Health Information Technology Adoption: The Potential for a New Digital Divide. J Med Internet Res. 2016;18:e264. doi: 10.2196/jmir.6349. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Tomer A, Fishbane L, Siefer A, et al. Digital Prosperity: How Broadband Can Delvier Health and Equity to All Communities. Metropolitan Infrastructure Initiative: Brookings Institution; 2020. [Google Scholar]
- 40.Sieck CJ, Sheon A, Ancker JS, et al. Digital inclusion as a social determinant of health. npj Digit Med. 2021;4:52. doi: 10.1038/s41746-021-00413-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Jacobson NC, Kowatsch T, Marsch LA. Digital Therapeutics for Mental Health and Addiction: The State of the Science and Vision for the Future. Academic Press; 2023. [Google Scholar]
- 42.Jackson DN, Sehgal N, Baur C. Benefits of mHealth Co-design for African American and Hispanic Adults: Multi-Method Participatory Research for a Health Information App. JMIR Form Res. 2022;6:e26764. doi: 10.2196/26764. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Nguyen KH, Fields JD, Cemballi AG, et al. The Role of Community-Based Organizations in Improving Chronic Care for Safety-Net Populations. J Am Board Fam Med. 2021;34:698–708. doi: 10.3122/jabfm.2021.04.200591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Burchert S, Alkneme MS, Bird M, et al. User-Centered App Adaptation of a Low-Intensity E-Mental Health Intervention for Syrian Refugees. Front Psychiatry. 2018;9:663. doi: 10.3389/fpsyt.2018.00663. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Juniar D, van Ballegooijen W, Karyotaki E, et al. Web-Based Stress Management Program for University Students in Indonesia: Systematic Cultural Adaptation and Protocol for a Feasibility Study. JMIR Res Protoc. 2019;8:e11493. doi: 10.2196/11493. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Sit HF, Ling R, Lam AIF, et al. The Cultural Adaptation of Step-by-Step: An Intervention to Address Depression Among Chinese Young Adults. Front Psychiatry. 2020;11:650. doi: 10.3389/fpsyt.2020.00650. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Naslund JA, Spagnolo J. Digital Therapeutics for Mental Health and Addiction. Elsevier; 2023. Cultural adaptations of digital therapeutics; pp. 151–64. [Google Scholar]
- 48.Friis-Healy EA, Nagy GA, Kollins SH. It Is Time to REACT: Opportunities for Digital Mental Health Apps to Reduce Mental Health Disparities in Racially and Ethnically Minoritized Groups. JMIR Ment Health. 2021;8:e25456. doi: 10.2196/25456. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Lyles CR, Nguyen OK, Khoong EC, et al. Multilevel Determinants of Digital Health Equity: A Literature Synthesis to Advance the Field. Annu Rev Public Health. 2023;44:383–405. doi: 10.1146/annurev-publhealth-071521-023913. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Richardson S, Lawrence K, Schoenthaler AM, et al. A framework for digital health equity. NPJ Digit Med. 2022;5:119. doi: 10.1038/s41746-022-00663-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Gallifant J, Nakayama LF, Gichoya JW, et al. Equity should be fundamental to the emergence of innovation. PLOS Digit Health. 2023;2:e0000224. doi: 10.1371/journal.pdig.0000224. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Jaworski BK, Webb Hooper M, Aklin WM, et al. Advancing digital health equity: Directions for behavioral and social science research. Transl Behav Med. 2023;13:132–9. doi: 10.1093/tbm/ibac088. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.The Lancet 50 years of the inverse care law. The Lancet. 2021;397:767. doi: 10.1016/S0140-6736(21)00505-5. [DOI] [PubMed] [Google Scholar]
- 54.Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8:19–32. doi: 10.1080/1364557032000119616. [DOI] [Google Scholar]
- 55.Munn Z, Peters MDJ, Stern C, et al. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18:143. doi: 10.1186/s12874-018-0611-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Peters MDJ, Godfrey C, McInerney P, et al. JBI Manual for Evidence Synthesis. JBI; 2020. Chapter 11: scoping reviews. [Google Scholar]
- 57.Tricco AC, Lillie E, Zarin W, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169:467–73. doi: 10.7326/M18-0850. [DOI] [PubMed] [Google Scholar]
- 58.Bitomsky L, Pfitzer EC, Nißen M, et al. Advancing health equity and the role of digital health technologies: a scoping review protocol. BMJ Open. 2024;14:e082336. doi: 10.1136/bmjopen-2023-082336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Shaw J, Brewer LC, Veinot T. Recommendations for Health Equity and Virtual Care Arising From the COVID-19 Pandemic: Narrative Review. JMIR Form Res. 2021;5:e23233. doi: 10.2196/23233. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Brown AF, Ma GX, Miranda J, et al. Structural Interventions to Reduce and Eliminate Health Disparities. Am J Public Health. 2019;109:S72–8. doi: 10.2105/AJPH.2018.304844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Agurs-Collins T, Persky S, Paskett ED, et al. Designing and Assessing Multilevel Interventions to Improve Minority Health and Reduce Health Disparities. Am J Public Health. 2019;109:S86–93. doi: 10.2105/AJPH.2018.304730. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Alvidrez J, Castille D, Laude-Sharp M, et al. The National Institute on Minority Health and Health Disparities Research Framework. Am J Public Health. 2019;109:S16–20. doi: 10.2105/AJPH.2018.304883. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Chunara R, Gjonaj J, Immaculate E, et al. Social determinants of health: the need for data science methods and capacity. Lancet Digit Health. 2024;6:e235–7. doi: 10.1016/S2589-7500(24)00022-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Nißen M, Harperink S, Joshi P, et al. Leveraging influencers to reach and engage vulnerable individuals: a quasi-experimental field study with a digital health intervention. 2025. [DOI]
- 65.Baah FO, Teitelman AM, Riegel B. Marginalization: Conceptualizing patient vulnerabilities in the framework of social determinants of health-An integrative review. Nurs Inq. 2019;26:e12268. doi: 10.1111/nin.12268. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Srinivasan S, Williams SD. Transitioning from health disparities to a health equity research agenda: the time is now. Public Health Rep. 2014;129 Suppl 2:71–6. doi: 10.1177/00333549141291S213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Areán PA, Allred R. Second Wave of Scalable Digital Therapeutics: Mental Health and Addiction Treatment Apps for Direct-to-Consumer Standalone Care. Elsevier; 2023. Digital therapeutics for mental health and addiction ; pp. 31–45. [Google Scholar]


